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Understanding 9/11
Cornelia van der Walt
https://2018.za.pycon.org/talks/67-insight-into-customer-segmentation/
In retail, understanding your customer is all, and when you do not have a brick and mortar storefront to attract new shoppers, it is even more important to get insight into the array of visitors who frequent your site. You need an idea of who they are, what they want and how to attract them. This is a universal truth of all businesses and the lessons learned here could be easily applied to other industries.
Enter data science and the ability to segment your customers. What is customer segmentation? What types of segmentation are there? What models could you use? How do you do it? What is it good for? Having done it a few times, first for Superbalist, then for the Spree customers during the Superbalist/Spree merger, I might have a few tricks and tips to share.
The talk will look at a high-level overview of clustering, then deep-dive into the code a bit before coming up at the end with a few use cases and conclusions. I'll discuss a few potential model algorithms we investigated, but focus mostly on the K-Means clustering model.
If you have some data science experience it would be helpful, but the talk should provide interesting information for everyone. The talk aims to leave you with a solid idea of how to build a customer segmentation model of your own. Come discover the joys of classification models with me!
pyconza2018
python
https://2018.za.pycon.org/talks/67-insight-into-customer-segmentation/
In retail, understanding your customer is all, and when you do not have a brick and mortar storefront to attract new shoppers, it is even more important to get insight into the array of visitors who frequent your site. You need an idea of who they are, what they want and how to attract them. This is a universal truth of all businesses and the lessons learned here could be easily applied to other industries.
Enter data science and the ability to segment your customers. What is customer segmentation? What types of segmentation are there? What models could you use? How do you do it? What is it good for? Having done it a few times, first for Superbalist, then for the Spree customers during the Superbalist/Spree merger, I might have a few tricks and tips to share.
The talk will look at a high-level overview of clustering, then deep-dive into the code a bit before coming up at the end with a few use cases and conclusions. I'll discuss a few potential model algorithms we investigated, but focus mostly on the K-Means clustering model.
If you have some data science experience it would be helpful, but the talk should provide interesting information for everyone. The talk aims to leave you with a solid idea of how to build a customer segmentation model of your own. Come discover the joys of classification models with me!
pyconza2018
python
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